\section{Conclusions \& Future Work}
\label{sec:futurework}
We have shown that combining evolutionary techniques with a graph representation of the ``Ms. Pac-Man'' maze world can yield good results. Our method learns the proper weight to assign to each element of the game and looks into the future to anticipate the movement of ghosts in the environment. These things give our agent the ability to adapt to the dynamic environment of the ``Ms. Pac-Man'' maze.

Considerable future work can be done into how the state of the game dynamically affects the weights of edges in the graph. While we simply look at the presence of an edible or inedible ghost between two nodes, the ghost's direction of movement can greatly impact its significance. An edible ghost that is moving towards ``Ms. Pac-Man'' is more desirable than one that is moving away. Likewise, an inedible ghost that is moving towards ``Ms. Pac-Man'' is a greater threat than an inedible ghost that is moving away from ``Ms. Pac-Man''.

Temporal changes can also affect the value that is placed on otherwise static elements in the game. Pills and power pills become more valuable as their availability (and the time remaining to collect them) decreases. This should probably affect the influence that such elements have when ``Ms. Pac-Man'' has been moving through a level for a long time. These are just some of the topics that could be explored more deeply in the future.
